Name
Abstract | ||
|---|---|---|
This paper is devoted to analysis of block multi-indexed higher-order covariance matrices, which can be used for the least-squares estimation problem. The formulation of linear and nonlinear least squares estimation problems is proposed, showing that their statements and solutions lead to generalized 'normal equations', employing covariance matrices of the underlying processes. Then, we provide a class of efficient algorithms to estimate higher-order statistics (generalized multi-indexed covariance matrices), which are necessary taking in mind practical aspects of the nonlinear treatment of the least-squares estimation problem. The algorithms are examined for different higher-order and non-Gaussian processes (time-series) and an impact of signal properties on covariance matrices is analysed. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.14736/kyb-2018-5-0865 | KYBERNETIKA |
Keywords | Field | DocType |
covariance matrix,higher-order statistics,adaptive,nonlinear | Mathematical optimization,Higher-order statistics,Stochastic process,Mathematics | Journal |
Volume | Issue | ISSN |
54 | 5 | 0023-5954 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wladyslaw Magiera | 1 | 0 | 0.68 |
| Urszula Libal | 2 | 0 | 0.68 |
| Agnieszka Wielgus | 3 | 0 | 0.68 |